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ASS-CD: Adapting Segment Anything Model and Swin-Transformer for Change Detection in Remote Sensing Images

Chenlong Wei,Xiaofeng Wu,Bin Wang

Published 2025 in Remote Sensing

ABSTRACT

Change detection (CD) is a critical task in analyzing the geographic information changes in remote sensing images (RSIs), yet it still faces challenges such as complex background interference, multi-scale varying objects, and class imbalance between positive and negative samples. Recently, with the development of pre-training and fine-tuning techniques, transferring the general knowledge embedded in large-scale pre-trained visual foundation models (PVFMs) to various downstream tasks has attracted significant attention. However, when directly applying these PVFMs to CD tasks in RSIs, the domain knowledge differences often result in unsatisfactory outcomes. To address the above issues, we propose a novel hierarchical adapter framework to efficiently adapt PVFMs like FastSAM and Swin-Transformer for CD task in RSIs, namely ASS-CD. The proposed method leverages lightweight adapter modules with a cross-attention mechanism, which not only preserves the general knowledge of PVFMs but also integrates global and local information, significantly enhancing CD accuracy. Further, the convolutional block attention module (CBAM) is adopted to reduce interference from complex backgrounds and focus on multi-scale objects, and the hierarchical deep supervision module (HDSM) is utilized to impose deep supervision on multi-scale feature maps and compute the Dice loss, addressing the issue of class imbalance in CD datasets. The experimental results on three widely used datasets demonstrate that our ASS-CD achieves the state-of-the-art performance, with an approximately 5% improvement on the LEVIR-CD dataset compared to the other CD methods.

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